arXiv:2606. 19135v1 Announce Type: cross Abstract: As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks.
arXiv:2510. 17149v3 Announce Type: replace Abstract: As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and reliability.
The paper introduces the Natural Language Interaction Protocol (NLIP), a standards‑based application‑layer protocol designed to enable AI agents built on diverse frameworks and tools to communicate seamlessly. NLIP offers a lightweight semantic message envelope that can be transmitted over common transports such as HTTP/HTTPS, WebSocket, and AMQP, and includes mechanisms for adapting between clients, agents, local context stores, ontologies, tools, and enterprise services. The authors discuss the protocol’s design rationale, security considerations, reference implementation, representative applications, adoption signals, and its relationship to other emerging agent protocols like MCP and A2A.
arXiv:2606. 17368v1 Announce Type: new Abstract: Large language models have accelerated the transition from passive conversational assistants to autonomous agents that can understand goals, plan actions, invoke tools, and execute multi-step tasks.
arXiv:2606. 14790v1 Announce Type: cross Abstract: LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited.
arXiv:2606. 02359v1 Announce Type: new Abstract: Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the equally critical problem: how to transmit and optimize messages among agents effectively?
arXiv:2606. 29601v1 Announce Type: cross Abstract: Current languages for specifying multiagent protocols either over-constrain protocol enactments or complicate capturing their meanings.
arXiv:2606. 30317v1 Announce Type: cross Abstract: The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services.
The paper introduces control‑data flow separation to improve prompt optimization in multi‑agent large language model systems. By representing execution protocols as typed, validated program objects and keeping task‑relevant content as unstructured language, the method prevents prompt edits from corrupting critical routing, formatting, or termination signals. Experiments on synthetic reasoning, collaborative review generation, and insurance rating workflows show that this approach maintains 100% protocol validity while consistently enhancing task performance.
arXiv:2608. 10714v1 Announce Type: cross Abstract: The Organic 6G vision of a network of networks spanning an edge-cloud continuum complemented by non-terrestrial resources requires, to realize its promise, service provisioning that is simple to operate, scalable across independently administered domains, and agile under domain churn (i.
The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services. Within months of release, hundreds of community-built MCP servers appeared on GitHub, but no software-maintenance literature has yet described how the ecosystem is being structured in production.
The paper proposes a scalable trust discovery architecture for the Internet of Agents, featuring a three‑layer hierarchical design: Agent Root for registry governance, Agent Registry for registration and metadata, and Agent Resolver for capability discovery. It introduces a registry‑suffix‑anchored composite identity scheme and a dual‑certificate, multi‑level authentication mechanism to strengthen agent identity trust. Prototype evaluation shows low latency (58 ms registration, 25 ms discovery) and high throughput (over 19,000 registrations and 29,000 discoveries per second).
The paper presents a formal analysis of four agent payment protocols—x402, MPP, ACP, and AP2—using the Tamarin prover. By modeling each protocol’s roles, state, and trust assumptions, the authors verify 86 cases, reproducing 46 known results and uncovering 40 new formal-consistency findings. They further validate ten findings through implementation proofs of concept, SDK/schema witnesses, and executable traces, highlighting the importance of consistent delegated authorization across all protocol stages.